Convolutional neural network for preprocessing-free bacterial Spectra identification

Identifying bacterial species is essential to epidemiological surveillance. However, the determination of bacterial species is a tedious and labor-intensive process. Various machine learning methods have been used for identifying bacterial species with mass spectral fingerprints. Although machine learning methods achieve real-time identification without human experts, it still requires data preprocessing. To address this issue, we proposed a unified solution for the identification of bacterial species with a convolutional neural network. The neural network automatically determined species according to their mass spectra without the preprocessing steps. The convolutional and pooling layers in the neural network could replace the binning, baseline correction, and scaling procedures. Moreover, because of the explainable structure, the model could identify important regions of spectra to discriminate each bacterial species. We used spectral samples obtained from the fatty acid methyl esters of 10 samples from 16 bacterial species (a total of 16) to demonstrate the usefulness of the proposed method by comparing it with existing classification methods preceded by preprocessing. The comparison results confirmed that the proposed method outperformed the alternatives in terms of classification accuracy and robustness. Moreover, the classification results of the proposed method are interpretable.